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dc.contributor.authorNatarajan, Rathika
dc.contributor.authorMehbodniya, Abolfazl
dc.contributor.authorRane, Kantilal Pitambar
dc.contributor.authorJindal, Sonika
dc.contributor.authorHasan, Mohammed Faez
dc.contributor.authorVives, Luis
dc.contributor.authorBhatt, Abhishek
dc.date.accessioned2022-01-18T14:57:21Z
dc.date.available2022-01-18T14:57:21Z
dc.date.issued2022-01-01
dc.identifier.issn01291831
dc.identifier.doi10.1142/S012918312250084X
dc.identifier.urihttp://hdl.handle.net/10757/658585
dc.descriptionEl texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.es_PE
dc.description.abstractOnline social media has made the process of disseminating news so quick that people have shifted their way of accessing news from traditional journalism and press to online social media sources. The rapid rotation of news on social media makes it challenging to evaluate its reliability. Fake news not only erodes public trust but also subverts their opinions. An intelligent automated system is required to detect fake news as there is a tenuous difference between fake and real news. This paper proposes an intelligent gravitational search random forest (IGSRF) algorithm to be employed to detect fake news. The IGSRF algorithm amalgamates the Intelligent Gravitational Search Algorithm (IGSA) and the Random Forest (RF) algorithm. The IGSA is an improved intelligent variant of the classical gravitational search algorithm (GSA) that adds information about the best and worst gravitational mass agents in order to retain the exploitation ability of agents at later iterations and thus avoid the trapping of the classical GSA in local optimum. In the proposed IGSRF algorithm, all the intelligent mass agents determine the solution by generating decision trees (DT) with a random subset of attributes following the hypothesis of random forest. The mass agents generate the collection of solutions from solution space using random proportional rules. The comprehensive prediction to decide the class of news (fake or real) is determined by all the agents following the attributes of random forest. The performance of the proposed algorithm is determined for the FakeNewsNet dataset, which has sub-categories of BuzzFeed and PolitiFact news categories. To analyze the effectiveness of the proposed algorithm, the results are also evaluated with decision tree and random forest algorithms. The proposed IGSRF algorithm has attained superlative results compared to the DT, RF and state-of-the-art techniques.es_PE
dc.formatapplication/htmles_PE
dc.language.isoenges_PE
dc.publisherWorld Scientifices_PE
dc.relation.urlhttps://www.worldscientific.com/doi/10.1142/S012918312250084Xes_PE
dc.rightsinfo:eu-repo/semantics/embargoedAccesses_PE
dc.sourceRepositorio Academico - UPCes_PE
dc.sourceUniversidad Peruana de Ciencias Aplicadas (UPC)es_PE
dc.subjectdecision treees_PE
dc.subjectfake newses_PE
dc.subjectfake news detectiones_PE
dc.subjectGravitational search algorithmes_PE
dc.subjectmeta-heuristices_PE
dc.subjectrandom forestes_PE
dc.titleIntelligent gravitational search random forest algorithm for fake news detectiones_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.journalInternational Journal of Modern Physics Ces_PE
dc.type.articleinfo:eu-repo/semantics/articlees_PE
dc.description.peerreviewRevisión por pareses_PE
dc.identifier.eid2-s2.0-85122250574
dc.identifier.scopusidSCOPUS_ID:85122250574
dc.source.journaltitleInternational Journal of Modern Physics C
dc.identifier.isni0000 0001 2196 144X


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